28 citations · 52 across the 12 of their papers we have counts for
5 papers · 1 filter
Context-Aware Multimodal Representation Learning for Spatio-Temporally Explicit Environmental Modelling
Julia Peters, Karin Mora, Miguel D. Mahecha +4
Earth observation (EO) foundation models have emerged as an effective approach to derive latent representations of the Earth system from various remote sensing sensors. These model…
Transformers vs. Recurrent Models for Estimating Forest Gross Primary Production
David Montero, Miguel D. Mahecha, Francesco Martinuzzi +6
Monitoring the spatiotemporal dynamics of forest CO uptake (Gross Primary Production, GPP), remains a central challenge in terrestrial ecosystem research. While Eddy Covariance…
Explainable Earth Surface Forecasting under Extreme Events
Oscar J. Pellicer-Valero, Miguel-Ángel Fernández-Torres, Chaonan Ji +2
With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystem…
DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremes
Chaonan Ji, Tonio Fincke, Vitus Benson +12
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show prom…
Recurrent Neural Networks for Modelling Gross Primary Production
David Montero, Miguel D. Mahecha, Francesco Martinuzzi +6
Accurate quantification of Gross Primary Production (GPP) is crucial for understanding terrestrial carbon dynamics. It represents the largest atmosphere-to-land CO flux, especi…